{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卷积神经网络（Convolutional Neural Network, CNN）\n",
    "\n",
    "## 项目：实现一个狗品种识别算法App\n",
    "\n",
    "在这个notebook文件中，有些模板代码已经提供给你，但你还需要实现更多的功能来完成这个项目。除非有明确要求，你无须修改任何已给出的代码。以**'(练习)'**开始的标题表示接下来的代码部分中有你需要实现的功能。这些部分都配有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示。\n",
    "\n",
    "除了实现代码外，你还**需要**回答一些与项目及代码相关的问题。每个需要回答的问题都会以 **'问题 X'** 标记。请仔细阅读每个问题，并且在问题后的 **'回答'** 部分写出完整的答案。我们将根据 你对问题的回答 和 撰写代码实现的功能 来对你提交的项目进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。\n",
    "\n",
    "项目中显示为_选做_的部分可以帮助你的项目脱颖而出，而不是仅仅达到通过的最低要求。如果你决定追求更高的挑战，请在此 notebook 中完成_选做_部分的代码。\n",
    "\n",
    "---\n",
    "\n",
    "### 让我们开始吧\n",
    "在这个notebook中，你将迈出第一步，来开发可以作为移动端或 Web应用程序一部分的算法。在这个项目的最后，你的程序将能够把用户提供的任何一个图像作为输入。如果可以从图像中检测到一只狗，它会输出对狗品种的预测。如果图像中是一个人脸，它会预测一个与其最相似的狗的种类。下面这张图展示了完成项目后可能的输出结果。（……实际上我们希望每个学生的输出结果不相同！）\n",
    "\n",
    "![Sample Dog Output](images/sample_dog_output.png)\n",
    "\n",
    "在现实世界中，你需要拼凑一系列的模型来完成不同的任务；举个例子，用来预测狗种类的算法会与预测人类的算法不同。在做项目的过程中，你可能会遇到不少失败的预测，因为并不存在完美的算法和模型。你最终提交的不完美的解决方案也一定会给你带来一个有趣的学习经验！\n",
    "\n",
    "### 项目内容\n",
    "\n",
    "我们将这个notebook分为不同的步骤，你可以使用下面的链接来浏览此notebook。\n",
    "\n",
    "* [Step 0](#step0): 导入数据集\n",
    "* [Step 1](#step1): 检测人脸\n",
    "* [Step 2](#step2): 检测狗狗\n",
    "* [Step 3](#step3): 从头创建一个CNN来分类狗品种\n",
    "* [Step 4](#step4): 使用一个CNN来区分狗的品种(使用迁移学习)\n",
    "* [Step 5](#step5): 建立一个CNN来分类狗的品种（使用迁移学习）\n",
    "* [Step 6](#step6): 完成你的算法\n",
    "* [Step 7](#step7): 测试你的算法\n",
    "\n",
    "在该项目中包含了如下的问题：\n",
    "\n",
    "* [问题 1](#question1)\n",
    "* [问题 2](#question2)\n",
    "* [问题 3](#question3)\n",
    "* [问题 4](#question4)\n",
    "* [问题 5](#question5)\n",
    "* [问题 6](#question6)\n",
    "* [问题 7](#question7)\n",
    "* [问题 8](#question8)\n",
    "* [问题 9](#question9)\n",
    "* [问题 10](#question10)\n",
    "* [问题 11](#question11)\n",
    "\n",
    "\n",
    "---\n",
    "<a id='step0'></a>\n",
    "## 步骤 0: 导入数据集\n",
    "\n",
    "### 导入狗数据集\n",
    "在下方的代码单元（cell）中，我们导入了一个狗图像的数据集。我们使用 scikit-learn 库中的 `load_files` 函数来获取一些变量：\n",
    "- `train_files`, `valid_files`, `test_files` - 包含图像的文件路径的numpy数组\n",
    "- `train_targets`, `valid_targets`, `test_targets` - 包含独热编码分类标签的numpy数组\n",
    "- `dog_names` - 由字符串构成的与标签相对应的狗的种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 133 total dog categories.\n",
      "There are 8351 total dog images.\n",
      "\n",
      "There are 6680 training dog images.\n",
      "There are 835 validation dog images.\n",
      "There are 836 test dog images.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# 定义函数来加载train，test和validation数据集\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# 加载train，test和validation数据集\n",
    "train_files, train_targets = load_dataset('dogImages/train')\n",
    "valid_files, valid_targets = load_dataset('dogImages/valid')\n",
    "test_files, test_targets = load_dataset('dogImages/test')\n",
    "\n",
    "# 加载狗品种列表\n",
    "dog_names = [item[20:-1] for item in sorted(glob(\"dogImages/train/*/\"))]\n",
    "\n",
    "# 打印数据统计描述\n",
    "print('There are %d total dog categories.' % len(dog_names))\n",
    "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training dog images.' % len(train_files))\n",
    "print('There are %d validation dog images.' % len(valid_files))\n",
    "print('There are %d test dog images.'% len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入人脸数据集\n",
    "\n",
    "在下方的代码单元中，我们导入人脸图像数据集，文件所在路径存储在名为 `human_files` 的 numpy 数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13233 total human images.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(8675309)\n",
    "\n",
    "# 加载打乱后的人脸数据集的文件名\n",
    "human_files = np.array(glob(\"lfw/*/*\"))\n",
    "random.shuffle(human_files)\n",
    "\n",
    "# 打印数据集的数据量\n",
    "print('There are %d total human images.' % len(human_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## 步骤1：检测人脸\n",
    " \n",
    "我们将使用 OpenCV 中的 [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) 来检测图像中的人脸。OpenCV 提供了很多预训练的人脸检测模型，它们以XML文件保存在 [github](https://github.com/opencv/opencv/tree/master/data/haarcascades)。我们已经下载了其中一个检测模型，并且把它存储在 `haarcascades` 的目录中。\n",
    "\n",
    "在如下代码单元中，我们将演示如何使用这个检测模型在样本图像中找到人脸。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of faces detected: 1\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2                \n",
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline                               \n",
    "\n",
    "# 提取预训练的人脸检测模型\n",
    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n",
    "\n",
    "# 加载彩色（通道顺序为BGR）图像\n",
    "img = cv2.imread(human_files[3])\n",
    "\n",
    "# 将BGR图像进行灰度处理\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# 在图像中找出脸\n",
    "faces = face_cascade.detectMultiScale(gray)\n",
    "\n",
    "# 打印图像中检测到的脸的个数\n",
    "print('Number of faces detected:', len(faces))\n",
    "\n",
    "# 获取每一个所检测到的脸的识别框\n",
    "for (x,y,w,h) in faces:\n",
    "    # 在人脸图像中绘制出识别框\n",
    "    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "    \n",
    "# 将BGR图像转变为RGB图像以打印\n",
    "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# 展示含有识别框的图像\n",
    "plt.imshow(cv_rgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用任何一个检测模型之前，将图像转换为灰度图是常用过程。`detectMultiScale` 函数使用储存在 `face_cascade` 中的的数据，对输入的灰度图像进行分类。\n",
    "\n",
    "在上方的代码中，`faces` 以 numpy 数组的形式，保存了识别到的面部信息。它其中每一行表示一个被检测到的脸，该数据包括如下四个信息：前两个元素  `x`、`y` 代表识别框左上角的 x 和 y 坐标（参照上图，注意 y 坐标的方向和我们默认的方向不同）；后两个元素代表识别框在 x 和 y 轴两个方向延伸的长度 `w` 和 `d`。 \n",
    "\n",
    "### 写一个人脸识别器\n",
    "\n",
    "我们可以将这个程序封装为一个函数。该函数的输入为人脸图像的**路径**，当图像中包含人脸时，该函数返回 `True`，反之返回 `False`。该函数定义如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果img_path路径表示的图像检测到了脸，返回\"True\" \n",
    "def face_detector(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray)\n",
    "    return len(faces) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **【练习】** 评估人脸检测模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "\n",
    "<a id='question1'></a>\n",
    "### __问题 1:__ \n",
    "\n",
    "在下方的代码块中，使用 `face_detector` 函数，计算：\n",
    "\n",
    "- `human_files` 的前100张图像中，能够检测到**人脸**的图像占比多少？\n",
    "- `dog_files` 的前100张图像中，能够检测到**人脸**的图像占比多少？\n",
    "\n",
    "理想情况下，人图像中检测到人脸的概率应当为100%，而狗图像中检测到人脸的概率应该为0%。你会发现我们的算法并非完美，但结果仍然是可以接受的。我们从每个数据集中提取前100个图像的文件路径，并将它们存储在`human_files_short`和`dog_files_short`中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human: 100.00%\n",
      "dog: 12.00%\n"
     ]
    }
   ],
   "source": [
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "## 请不要修改上方代码\n",
    "\n",
    "\n",
    "## TODO: 基于human_files_short和dog_files_short中的图像测试face_detector的表现\n",
    "def detect(detector, files):\n",
    "    return sum([1 if detector(f) else 0 for f in files]) / len(files)\n",
    "print('human: {:.2f}%'.format(detect(face_detector, human_files_short) * 100))\n",
    "print('dog: {:.2f}%'.format(detect(face_detector, dog_files_short) * 100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='question2'></a>\n",
    "\n",
    "### __问题 2:__ \n",
    "\n",
    "就算法而言，该算法成功与否的关键在于，用户能否提供含有清晰面部特征的人脸图像。\n",
    "那么你认为，这样的要求在实际使用中对用户合理吗？如果你觉得不合理，你能否想到一个方法，即使图像中并没有清晰的面部特征，也能够检测到人脸？\n",
    "\n",
    "__回答:__\n",
    "\n",
    "1、借助Kaggle提供的人脸部位识别挑战赛的数据库，包含15个关键点；或者一个更复杂些的数据库MUCT，它有76个关键点。\n",
    "\n",
    "2、使用OpenCV技术可将图像中的人脸框出来，对每一个返回的人脸框，我们提取其中相应的子图像，将它们调整到灰度图像并转换尺寸。新产生的图像数据作为CNN网络的输入。同时使用Max Pooling来避免过拟合。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='Selection1'></a>\n",
    "### 选做：\n",
    "\n",
    "我们建议在你的算法中使用opencv的人脸检测模型去检测人类图像，不过你可以自由地探索其他的方法，尤其是尝试使用深度学习来解决它:)。请用下方的代码单元来设计和测试你的面部监测算法。如果你决定完成这个_选做_任务，你需要报告算法在每一个数据集上的表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## (选做) TODO: 报告另一个面部检测算法在LFW数据集上的表现\n",
    "### 你可以随意使用所需的代码单元数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "\n",
    "## 步骤 2: 检测狗狗\n",
    "\n",
    "在这个部分中，我们使用预训练的 [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) 模型去检测图像中的狗。下方的第一行代码就是下载了 ResNet-50 模型的网络结构参数，以及基于 [ImageNet](http://www.image-net.org/) 数据集的预训练权重。\n",
    "\n",
    "ImageNet 这目前一个非常流行的数据集，常被用来测试图像分类等计算机视觉任务相关的算法。它包含超过一千万个 URL，每一个都链接到 [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中所对应的一个物体的图像。任给输入一个图像，该 ResNet-50 模型会返回一个对图像中物体的预测结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "\n",
    "# 定义ResNet50模型\n",
    "ResNet50_model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "\n",
    "- 在使用 TensorFlow 作为后端的时候，在 Keras 中，CNN 的输入是一个4维数组（也被称作4维张量），它的各维度尺寸为 `(nb_samples, rows, columns, channels)`。其中 `nb_samples` 表示图像（或者样本）的总数，`rows`, `columns`, 和 `channels` 分别表示图像的行数、列数和通道数。\n",
    "\n",
    "\n",
    "- 下方的 `path_to_tensor` 函数实现如下将彩色图像的字符串型的文件路径作为输入，返回一个4维张量，作为 Keras CNN 输入。因为我们的输入图像是彩色图像，因此它们具有三个通道（ `channels` 为 `3`）。\n",
    "    1. 该函数首先读取一张图像，然后将其缩放为 224×224 的图像。\n",
    "    2. 随后，该图像被调整为具有4个维度的张量。\n",
    "    3. 对于任一输入图像，最后返回的张量的维度是：`(1, 224, 224, 3)`。\n",
    "\n",
    "\n",
    "- `paths_to_tensor` 函数将图像路径的字符串组成的 numpy 数组作为输入，并返回一个4维张量，各维度尺寸为 `(nb_samples, 224, 224, 3)`。 在这里，`nb_samples`是提供的图像路径的数据中的样本数量或图像数量。你也可以将 `nb_samples` 理解为数据集中3维张量的个数（每个3维张量表示一个不同的图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # 用PIL加载RGB图像为PIL.Image.Image类型\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # 将PIL.Image.Image类型转化为格式为(224, 224, 3)的3维张量\n",
    "    x = image.img_to_array(img)\n",
    "    # 将3维张量转化为格式为(1, 224, 224, 3)的4维张量并返回\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基于 ResNet-50 架构进行预测\n",
    "\n",
    "对于通过上述步骤得到的四维张量，在把它们输入到 ResNet-50 网络、或 Keras 中其他类似的预训练模型之前，还需要进行一些额外的处理：\n",
    "1. 首先，这些图像的通道顺序为 RGB，我们需要重排他们的通道顺序为 BGR。\n",
    "2. 其次，预训练模型的输入都进行了额外的归一化过程。因此我们在这里也要对这些张量进行归一化，即对所有图像所有像素都减去像素均值 `[103.939, 116.779, 123.68]`（以 RGB 模式表示，根据所有的 ImageNet 图像算出）。\n",
    "\n",
    "导入的 `preprocess_input` 函数实现了这些功能。如果你对此很感兴趣，可以在 [这里](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py) 查看 `preprocess_input`的代码。\n",
    "\n",
    "\n",
    "在实现了图像处理的部分之后，我们就可以使用模型来进行预测。这一步通过 `predict` 方法来实现，它返回一个向量，向量的第 i 个元素表示该图像属于第 i 个 ImageNet 类别的概率。这通过如下的 `ResNet50_predict_labels` 函数实现。\n",
    "\n",
    "通过对预测出的向量取用 argmax 函数（找到有最大概率值的下标序号），我们可以得到一个整数，即模型预测到的物体的类别。进而根据这个 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)，我们能够知道这具体是哪个品种的狗狗。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "def ResNet50_predict_labels(img_path):\n",
    "    # 返回img_path路径的图像的预测向量\n",
    "    img = preprocess_input(path_to_tensor(img_path))\n",
    "    return np.argmax(ResNet50_model.predict(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 完成狗检测模型\n",
    "\n",
    "\n",
    "在研究该 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 的时候，你会注意到，狗类别对应的序号为151-268。因此，在检查预训练模型判断图像是否包含狗的时候，我们只需要检查如上的 `ResNet50_predict_labels` 函数是否返回一个介于151和268之间（包含区间端点）的值。\n",
    "\n",
    "我们通过这些想法来完成下方的 `dog_detector` 函数，如果从图像中检测到狗就返回 `True`，否则返回 `False`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dog_detector(img_path):\n",
    "    prediction = ResNet50_predict_labels(img_path)\n",
    "    return ((prediction <= 268) & (prediction >= 151)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【作业】评估狗狗检测模型\n",
    "\n",
    "---\n",
    "\n",
    "<a id='question3'></a>\n",
    "### __问题 3:__ \n",
    "\n",
    "在下方的代码块中，使用 `dog_detector` 函数，计算：\n",
    "\n",
    "- `human_files_short`中图像检测到狗狗的百分比？\n",
    "- `dog_files_short`中图像检测到狗狗的百分比？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human: 0.00%\n",
      "dog: 100.00%\n"
     ]
    }
   ],
   "source": [
    "### TODO: 测试dog_detector函数在human_files_short和dog_files_short的表现\n",
    "print('human: {:.2f}%'.format(detect(dog_detector, human_files_short) * 100))\n",
    "print('dog: {:.2f}%'.format(detect(dog_detector, dog_files_short) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='step3'></a>\n",
    "\n",
    "## 步骤 3: 从头开始创建一个CNN来分类狗品种\n",
    "\n",
    "\n",
    "现在我们已经实现了一个函数，能够在图像中识别人类及狗狗。但我们需要更进一步的方法，来对狗的类别进行识别。在这一步中，你需要实现一个卷积神经网络来对狗的品种进行分类。你需要__从头实现__你的卷积神经网络（在这一阶段，你还不能使用迁移学习），并且你需要达到超过1%的测试集准确率。在本项目的步骤五种，你还有机会使用迁移学习来实现一个准确率大大提高的模型。\n",
    "\n",
    "在添加卷积层的时候，注意不要加上太多的（可训练的）层。更多的参数意味着更长的训练时间，也就是说你更可能需要一个 GPU 来加速训练过程。万幸的是，Keras 提供了能够轻松预测每次迭代（epoch）花费时间所需的函数。你可以据此推断你算法所需的训练时间。\n",
    "\n",
    "值得注意的是，对狗的图像进行分类是一项极具挑战性的任务。因为即便是一个正常人，也很难区分布列塔尼犬和威尔士史宾格犬。\n",
    "\n",
    "\n",
    "布列塔尼犬（Brittany） | 威尔士史宾格犬（Welsh Springer Spaniel）\n",
    "- | - \n",
    "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
    "\n",
    "不难发现其他的狗品种会有很小的类间差别（比如金毛寻回犬和美国水猎犬）。\n",
    "\n",
    "\n",
    "金毛寻回犬（Curly-Coated Retriever） | 美国水猎犬（American Water Spaniel）\n",
    "- | -\n",
    "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n",
    "\n",
    "同样，拉布拉多犬（labradors）有黄色、棕色和黑色这三种。那么你设计的基于视觉的算法将不得不克服这种较高的类间差别，以达到能够将这些不同颜色的同类狗分到同一个品种中。\n",
    "\n",
    "黄色拉布拉多犬（Yellow Labrador） | 棕色拉布拉多犬（Chocolate Labrador） | 黑色拉布拉多犬（Black Labrador）\n",
    "- | -\n",
    "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n",
    "\n",
    "我们也提到了随机分类将得到一个非常低的结果：不考虑品种略有失衡的影响，随机猜测到正确品种的概率是1/133，相对应的准确率是低于1%的。\n",
    "\n",
    "请记住，在深度学习领域，实践远远高于理论。大量尝试不同的框架吧，相信你的直觉！当然，玩得开心！\n",
    "\n",
    "\n",
    "### 数据预处理\n",
    "\n",
    "\n",
    "通过对每张图像的像素值除以255，我们对图像实现了归一化处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6680/6680 [01:07<00:00, 98.35it/s] \n",
      "100%|██████████| 835/835 [00:08<00:00, 93.68it/s] \n",
      "100%|██████████| 836/836 [00:09<00:00, 51.49it/s] \n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFile                            \n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True                 \n",
    "\n",
    "# Keras中的数据预处理过程\n",
    "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n",
    "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n",
    "test_tensors = paths_to_tensor(test_files).astype('float32')/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【练习】模型架构\n",
    "\n",
    "\n",
    "创建一个卷积神经网络来对狗品种进行分类。在你代码块的最后，执行 `model.summary()` 来输出你模型的总结信息。\n",
    "    \n",
    "我们已经帮你导入了一些所需的 Python 库，如有需要你可以自行导入。如果你在过程中遇到了困难，如下是给你的一点小提示——该模型能够在5个 epoch 内取得超过1%的测试准确率，并且能在CPU上很快地训练。\n",
    "\n",
    "![Sample CNN](images/sample_cnn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='question4'></a>  \n",
    "\n",
    "### __问题 4:__ \n",
    "\n",
    "在下方的代码块中尝试使用 Keras 搭建卷积网络的架构，并回答相关的问题。\n",
    "\n",
    "1. 你可以尝试自己搭建一个卷积网络的模型，那么你需要回答你搭建卷积网络的具体步骤（用了哪些层）以及为什么这样搭建。\n",
    "2. 你也可以根据上图提示的步骤搭建卷积网络，那么请说明为何如上的架构能够在该问题上取得很好的表现。\n",
    "\n",
    "__回答:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 112, 112, 16)      64        \n",
      "_________________________________________________________________\n",
      "activation_50 (Activation)   (None, 112, 112, 16)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 112, 112, 32)      2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 56, 56, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_51 (Activation)   (None, 56, 56, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 56, 56, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_52 (Activation)   (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 133)               8645      \n",
      "=================================================================\n",
      "Total params: 19,637\n",
      "Trainable params: 19,413\n",
      "Non-trainable params: 224\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Activation, GlobalAveragePooling2D, Dropout\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "### TODO: 定义你的网络架构\n",
    "model.add(Conv2D(16, (2, 2), strides=(1, 1), padding='same', input_shape=(224, 224, 3)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(32, (2, 2), strides=(1, 1), padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(64, (2, 2), strides=(1, 1), padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dropout(0.3))\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型构建说明\n",
    "- 该网络以三个卷积层（后面跟着最大池化层）序列开始，旨在将图片像素数组输入转换为所有空间信息都丢失、仅保留图片内容信息的数组，filters数量递增（16、32、64），kernel_size 卷积窗口为 2 。\n",
    "- MaxPooling（最大池化层），使得空间维度下降，变成上一层的一半，通过组合使用卷基层和最大池化层，我们可以获得很深，且空间维度很小的数组。\n",
    "- 添加BatchNormalization层来降低Covariate Shift并加速运算过程。\n",
    "- GlobalAveragePooling2D 替代 Flatten，减少了参数的使用量，避免了过拟合现象。\n",
    "- 添加dropout层可以很有效的避免模型过拟合。\n",
    "- 网络中的最后层级是具有 softmax 激活函数的密集层，最后层级的节点数量应该等于狗狗数据集中的类别总数，针对数据集中的每个对象类别都有一个条目，使其返回概率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 编译模型\n",
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 【练习】训练模型\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "<a id='question5'></a>  \n",
    "\n",
    "### __问题 5:__ \n",
    "\n",
    "在下方代码单元训练模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n",
    "\n",
    "可选题：你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)，来优化模型的表现。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/5\n",
      "6680/6680 [==============================] - 319s 48ms/step - loss: 4.9143 - acc: 0.0121 - val_loss: 4.8257 - val_acc: 0.0240\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 4.82569, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "Epoch 2/5\n",
      "6680/6680 [==============================] - 305s 46ms/step - loss: 4.8075 - acc: 0.0201 - val_loss: 4.7550 - val_acc: 0.0240\n",
      "\n",
      "Epoch 00002: val_loss improved from 4.82569 to 4.75499, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "Epoch 3/5\n",
      "6680/6680 [==============================] - 297s 44ms/step - loss: 4.7327 - acc: 0.0260 - val_loss: 4.6893 - val_acc: 0.0311\n",
      "\n",
      "Epoch 00003: val_loss improved from 4.75499 to 4.68927, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "Epoch 4/5\n",
      "6680/6680 [==============================] - 294s 44ms/step - loss: 4.6718 - acc: 0.0322 - val_loss: 4.6706 - val_acc: 0.0395\n",
      "\n",
      "Epoch 00004: val_loss improved from 4.68927 to 4.67056, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "Epoch 5/5\n",
      "6680/6680 [==============================] - 354s 53ms/step - loss: 4.6308 - acc: 0.0311 - val_loss: 4.6222 - val_acc: 0.0287\n",
      "\n",
      "Epoch 00005: val_loss improved from 4.67056 to 4.62215, saving model to saved_models/weights.best.from_scratch.hdf5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a37094278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint  \n",
    "\n",
    "### TODO: 设置训练模型的epochs的数量\n",
    "\n",
    "epochs = 5\n",
    "\n",
    "### 不要修改下方代码\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "model.fit(train_tensors, train_targets, \n",
    "          validation_data=(valid_tensors, valid_targets),\n",
    "          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 加载具有最好验证loss的模型\n",
    "\n",
    "model.load_weights('saved_models/weights.best.from_scratch.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型\n",
    "\n",
    "在狗图像的测试数据集上试用你的模型。确保测试准确率大于1%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 3.5885%\n"
     ]
    }
   ],
   "source": [
    "# 获取测试数据集中每一个图像所预测的狗品种的index\n",
    "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "# 报告测试准确率\n",
    "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step4'></a>\n",
    "## 步骤 4: 使用一个CNN来区分狗的品种\n",
    "\n",
    "\n",
    "使用 迁移学习（Transfer Learning）的方法，能帮助我们在不损失准确率的情况下大大减少训练时间。在以下步骤中，你可以尝试使用迁移学习来训练你自己的CNN。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 得到从图像中提取的特征向量（Bottleneck Features）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')\n",
    "train_VGG16 = bottleneck_features['train']\n",
    "valid_VGG16 = bottleneck_features['valid']\n",
    "test_VGG16 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型架构\n",
    "\n",
    "该模型使用预训练的 VGG-16 模型作为固定的图像特征提取器，其中 VGG-16 最后一层卷积层的输出被直接输入到我们的模型。我们只需要添加一个全局平均池化层以及一个全连接层，其中全连接层使用 softmax 激活函数，对每一个狗的种类都包含一个节点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_3 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 133)               68229     \n",
      "=================================================================\n",
      "Total params: 68,229\n",
      "Trainable params: 68,229\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "VGG16_model = Sequential()\n",
    "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n",
    "VGG16_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "VGG16_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 编译模型\n",
    "\n",
    "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/20\n",
      "6680/6680 [==============================] - 2s 317us/step - loss: 12.1687 - acc: 0.1292 - val_loss: 10.6801 - val_acc: 0.2036\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 10.68007, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 2/20\n",
      "6680/6680 [==============================] - 1s 211us/step - loss: 9.5750 - acc: 0.2909 - val_loss: 9.3087 - val_acc: 0.3054\n",
      "\n",
      "Epoch 00002: val_loss improved from 10.68007 to 9.30871, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 3/20\n",
      "6680/6680 [==============================] - 1s 188us/step - loss: 8.4849 - acc: 0.3895 - val_loss: 8.7530 - val_acc: 0.3581\n",
      "\n",
      "Epoch 00003: val_loss improved from 9.30871 to 8.75300, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 4/20\n",
      "6680/6680 [==============================] - 1s 207us/step - loss: 8.1392 - acc: 0.4350 - val_loss: 8.6378 - val_acc: 0.3629\n",
      "\n",
      "Epoch 00004: val_loss improved from 8.75300 to 8.63776, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 5/20\n",
      "6680/6680 [==============================] - 1s 214us/step - loss: 7.9429 - acc: 0.4603 - val_loss: 8.4969 - val_acc: 0.3749\n",
      "\n",
      "Epoch 00005: val_loss improved from 8.63776 to 8.49686, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 6/20\n",
      "6680/6680 [==============================] - 2s 229us/step - loss: 7.5461 - acc: 0.4825 - val_loss: 7.8975 - val_acc: 0.4263\n",
      "\n",
      "Epoch 00006: val_loss improved from 8.49686 to 7.89755, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 7/20\n",
      "6680/6680 [==============================] - 1s 205us/step - loss: 7.2180 - acc: 0.5154 - val_loss: 7.8514 - val_acc: 0.4311\n",
      "\n",
      "Epoch 00007: val_loss improved from 7.89755 to 7.85141, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 8/20\n",
      "6680/6680 [==============================] - 1s 210us/step - loss: 7.0837 - acc: 0.5292 - val_loss: 7.6319 - val_acc: 0.4359\n",
      "\n",
      "Epoch 00008: val_loss improved from 7.85141 to 7.63185, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 9/20\n",
      "6680/6680 [==============================] - 2s 235us/step - loss: 6.7521 - acc: 0.5445 - val_loss: 7.3681 - val_acc: 0.4491\n",
      "\n",
      "Epoch 00009: val_loss improved from 7.63185 to 7.36812, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 10/20\n",
      "6680/6680 [==============================] - 1s 211us/step - loss: 6.5097 - acc: 0.5645 - val_loss: 7.1724 - val_acc: 0.4778\n",
      "\n",
      "Epoch 00010: val_loss improved from 7.36812 to 7.17244, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 11/20\n",
      "6680/6680 [==============================] - 1s 206us/step - loss: 6.4052 - acc: 0.5792 - val_loss: 7.1480 - val_acc: 0.4707\n",
      "\n",
      "Epoch 00011: val_loss improved from 7.17244 to 7.14800, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 12/20\n",
      "6680/6680 [==============================] - 1s 210us/step - loss: 6.2915 - acc: 0.5907 - val_loss: 7.0575 - val_acc: 0.4850\n",
      "\n",
      "Epoch 00012: val_loss improved from 7.14800 to 7.05750, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 13/20\n",
      "6680/6680 [==============================] - 2s 228us/step - loss: 6.2328 - acc: 0.5982 - val_loss: 7.0269 - val_acc: 0.4754\n",
      "\n",
      "Epoch 00013: val_loss improved from 7.05750 to 7.02686, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 14/20\n",
      "6680/6680 [==============================] - 2s 249us/step - loss: 6.0478 - acc: 0.6043 - val_loss: 7.0078 - val_acc: 0.4874\n",
      "\n",
      "Epoch 00014: val_loss improved from 7.02686 to 7.00780, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 15/20\n",
      "6680/6680 [==============================] - 2s 237us/step - loss: 5.9417 - acc: 0.6187 - val_loss: 6.8527 - val_acc: 0.4886\n",
      "\n",
      "Epoch 00015: val_loss improved from 7.00780 to 6.85272, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 16/20\n",
      "6680/6680 [==============================] - 1s 206us/step - loss: 5.9087 - acc: 0.6240 - val_loss: 6.7670 - val_acc: 0.5138\n",
      "\n",
      "Epoch 00016: val_loss improved from 6.85272 to 6.76704, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 17/20\n",
      "6680/6680 [==============================] - 1s 209us/step - loss: 5.8539 - acc: 0.6269 - val_loss: 6.8199 - val_acc: 0.5006\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      "Epoch 18/20\n",
      "6680/6680 [==============================] - 1s 207us/step - loss: 5.8169 - acc: 0.6296 - val_loss: 6.6807 - val_acc: 0.4982\n",
      "\n",
      "Epoch 00018: val_loss improved from 6.76704 to 6.68073, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 19/20\n",
      "6680/6680 [==============================] - 1s 215us/step - loss: 5.7723 - acc: 0.6314 - val_loss: 6.6642 - val_acc: 0.5054\n",
      "\n",
      "Epoch 00019: val_loss improved from 6.68073 to 6.66418, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "Epoch 20/20\n",
      "6680/6680 [==============================] - 1s 213us/step - loss: 5.6631 - acc: 0.6376 - val_loss: 6.5740 - val_acc: 0.5090\n",
      "\n",
      "Epoch 00020: val_loss improved from 6.66418 to 6.57404, saving model to saved_models/weights.best.VGG16.hdf5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a2e26c4e0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 训练模型\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "VGG16_model.fit(train_VGG16, train_targets, \n",
    "          validation_data=(valid_VGG16, valid_targets),\n",
    "          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 加载具有最好验证loss的模型\n",
    "\n",
    "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试模型\n",
    "现在，我们可以测试此CNN在狗图像测试数据集中识别品种的效果如何。我们在下方打印出测试准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 50.2392%\n"
     ]
    }
   ],
   "source": [
    "# 获取测试数据集中每一个图像所预测的狗品种的index\n",
    "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n",
    "\n",
    "# 报告测试准确率\n",
    "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用模型预测狗的品种"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def VGG16_predict_breed(img_path):\n",
    "    # 提取bottleneck特征\n",
    "    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n",
    "    # 获取预测向量\n",
    "    predicted_vector = VGG16_model.predict(bottleneck_feature)\n",
    "    # 返回此模型预测的狗的品种\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "## 步骤 5: 建立一个CNN来分类狗的品种（使用迁移学习）\n",
    "\n",
    "现在你将使用迁移学习来建立一个CNN，从而可以从图像中识别狗的品种。你的 CNN 在测试集上的准确率必须至少达到60%。\n",
    "\n",
    "在步骤4中，我们使用了迁移学习来创建一个使用基于 VGG-16 提取的特征向量来搭建一个 CNN。在本部分内容中，你必须使用另一个预训练模型来搭建一个 CNN。为了让这个任务更易实现，我们已经预先对目前 keras 中可用的几种网络进行了预训练：\n",
    "\n",
    "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n",
    "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n",
    "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n",
    "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n",
    "\n",
    "这些文件被命名为为：\n",
    "\n",
    "    Dog{network}Data.npz\n",
    "\n",
    "其中 `{network}` 可以是 `VGG19`、`Resnet50`、`InceptionV3` 或 `Xception` 中的一个。选择上方网络架构中的一个，下载相对应的bottleneck特征，并将所下载的文件保存在目录 `bottleneck_features/` 中。\n",
    "\n",
    "\n",
    "### 【练习】获取模型的特征向量\n",
    "\n",
    "在下方代码块中，通过运行下方代码提取训练、测试与验证集相对应的bottleneck特征。\n",
    "\n",
    "    bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')\n",
    "    train_{network} = bottleneck_features['train']\n",
    "    valid_{network} = bottleneck_features['valid']\n",
    "    test_{network} = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 从另一个预训练的CNN获取bottleneck特征\n",
    "bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')\n",
    "train_Resnet50 = bottleneck_features['train']\n",
    "valid_Resnet50 = bottleneck_features['valid']\n",
    "test_Resnet50 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【练习】模型架构\n",
    "\n",
    "建立一个CNN来分类狗品种。在你的代码单元块的最后，通过运行如下代码输出网络的结构：\n",
    "    \n",
    "        <your model's name>.summary()\n",
    "   \n",
    "---\n",
    "\n",
    "<a id='question6'></a>  \n",
    "\n",
    "### __问题 6:__ \n",
    "\n",
    "\n",
    "在下方的代码块中尝试使用 Keras 搭建最终的网络架构，并回答你实现最终 CNN 架构的步骤与每一步的作用，并描述你在迁移学习过程中，使用该网络架构的原因。\n",
    "\n",
    "\n",
    "__回答:__ \n",
    "\n",
    "Resnet（残差网络），与传统的顺序网络架构（如AlexNet、OverFeat和VGG）不同，可以让网络在深度增加情况下却不退化。VGG, Resnet 等 CNN 网络都是图片分类网络, 都是在 imagenet 上 1.2 million 数据训练出来的。Resnet 比起 VGG 单在 imagenet 上的分类结果就要好大概50%，这就是为何此模型训练的结果比第三步中的模型高很多，而且训练速度非常快。\n",
    "\n",
    "- 添加BatchNormalization层来降低Covariate Shift并加速运算过程\n",
    "- 添加dropout层可以很有效的避免模型过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_4 ( (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 133)               272517    \n",
      "=================================================================\n",
      "Total params: 280,709\n",
      "Trainable params: 276,613\n",
      "Non-trainable params: 4,096\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### TODO: 定义你的框架\n",
    "Resnet50_model = Sequential()\n",
    "Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))\n",
    "Resnet50_model.add(BatchNormalization())\n",
    "Resnet50_model.add(Dropout(0.4))\n",
    "Resnet50_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "Resnet50_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 编译模型\n",
    "Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 【练习】训练模型\n",
    "\n",
    "<a id='question7'></a>  \n",
    "\n",
    "### __问题 7:__ \n",
    "\n",
    "在下方代码单元中训练你的模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n",
    "\n",
    "当然，你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) 以优化模型的表现，不过这不是必须的步骤。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/8\n",
      "6680/6680 [==============================] - 3s 393us/step - loss: 0.2871 - acc: 0.9262 - val_loss: 0.7619 - val_acc: 0.7916\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.76187, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "Epoch 2/8\n",
      "6680/6680 [==============================] - 2s 361us/step - loss: 0.2364 - acc: 0.9368 - val_loss: 0.7844 - val_acc: 0.8024\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "Epoch 3/8\n",
      "6680/6680 [==============================] - 2s 367us/step - loss: 0.1981 - acc: 0.9458 - val_loss: 0.7254 - val_acc: 0.8024\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.76187 to 0.72535, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "Epoch 4/8\n",
      "6680/6680 [==============================] - 2s 363us/step - loss: 0.1556 - acc: 0.9593 - val_loss: 0.7505 - val_acc: 0.8084\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "Epoch 5/8\n",
      "6680/6680 [==============================] - 2s 371us/step - loss: 0.1271 - acc: 0.9686 - val_loss: 0.7199 - val_acc: 0.8072\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.72535 to 0.71991, saving model to saved_models/weights.best.Resnet50.hdf5\n",
      "Epoch 6/8\n",
      "6680/6680 [==============================] - 2s 361us/step - loss: 0.1190 - acc: 0.9684 - val_loss: 0.7456 - val_acc: 0.7988\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "Epoch 7/8\n",
      "6680/6680 [==============================] - 2s 362us/step - loss: 0.1056 - acc: 0.9737 - val_loss: 0.7561 - val_acc: 0.8036\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "Epoch 8/8\n",
      "6680/6680 [==============================] - 2s 368us/step - loss: 0.0957 - acc: 0.9731 - val_loss: 0.7566 - val_acc: 0.7976\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a2f53f0f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TODO: 训练模型\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "Resnet50_model.fit(train_Resnet50, train_targets, \n",
    "          validation_data=(valid_Resnet50, valid_targets),\n",
    "          epochs=8, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 加载具有最佳验证loss的模型权重\n",
    "\n",
    "Resnet50_model.load_weights('saved_models/weights.best.Resnet50.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 【练习】测试模型\n",
    "\n",
    "<a id='question8'></a>  \n",
    "\n",
    "### __问题 8:__ \n",
    "\n",
    "在狗图像的测试数据集上试用你的模型。确保测试准确率大于60%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 80.3828%\n"
     ]
    }
   ],
   "source": [
    "### TODO: 在测试集上计算分类准确率\n",
    "\n",
    "Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]\n",
    "\n",
    "# 报告测试准确率\n",
    "test_accuracy = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 【练习】使用模型测试狗的品种\n",
    "\n",
    "\n",
    "实现一个函数，它的输入为图像路径，功能为预测对应图像的类别，输出为你模型预测出的狗类别（`Affenpinscher`, `Afghan_hound` 等）。\n",
    "\n",
    "与步骤5中的模拟函数类似，你的函数应当包含如下三个步骤：\n",
    "\n",
    "1. 根据选定的模型载入图像特征（bottleneck features）\n",
    "2. 将图像特征输输入到你的模型中，并返回预测向量。注意，在该向量上使用 argmax 函数可以返回狗种类的序号。\n",
    "3. 使用在步骤0中定义的 `dog_names` 数组来返回对应的狗种类名称。\n",
    "\n",
    "提取图像特征过程中使用到的函数可以在 `extract_bottleneck_features.py` 中找到。同时，他们应已在之前的代码块中被导入。根据你选定的 CNN 网络，你可以使用 `extract_{network}` 函数来获得对应的图像特征，其中 `{network}` 代表 `VGG19`, `Resnet50`, `InceptionV3`, 或 `Xception` 中的一个。\n",
    " \n",
    "---\n",
    "\n",
    "<a id='question9'></a>  \n",
    "\n",
    "### __问题 9:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 写一个函数，该函数将图像的路径作为输入\n",
    "### 然后返回此模型所预测的狗的品种\n",
    "from extract_bottleneck_features import *\n",
    "\n",
    "def Resnet50_predict_breed(img_path):\n",
    "    # 提取bottleneck特征\n",
    "    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))\n",
    "    # 获取预测向量\n",
    "    predicted_vector = Resnet50_model.predict(bottleneck_feature)\n",
    "    # 返回此模型预测的狗的品种\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='step6'></a>\n",
    "## 步骤 6: 完成你的算法\n",
    "\n",
    "\n",
    "\n",
    "实现一个算法，它的输入为图像的路径，它能够区分图像是否包含一个人、狗或两者都不包含，然后：\n",
    "\n",
    "- 如果从图像中检测到一只__狗__，返回被预测的品种。\n",
    "- 如果从图像中检测到__人__，返回最相像的狗品种。\n",
    "- 如果两者都不能在图像中检测到，输出错误提示。\n",
    "\n",
    "我们非常欢迎你来自己编写检测图像中人类与狗的函数，你可以随意地使用上方完成的 `face_detector` 和 `dog_detector` 函数。你__需要__在步骤5使用你的CNN来预测狗品种。\n",
    "\n",
    "下面提供了算法的示例输出，但你可以自由地设计自己的模型！\n",
    "\n",
    "![Sample Human Output](images/sample_human_output.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<a id='question10'></a>  \n",
    "\n",
    "### __问题 10:__\n",
    "\n",
    "在下方代码块中完成你的代码。\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TODO: 设计你的算法\n",
    "### 自由地使用所需的代码单元数吧\n",
    "def cnn_predict_func(img_path):\n",
    "    predict_result = ''\n",
    "    if dog_detector(img_path):\n",
    "        predict_result = 'find a dog, it looks like a... \\n' + Resnet50_predict_breed(img_path)\n",
    "    elif face_detector(img_path):\n",
    "        predict_result = 'hello human, you look like a ... \\n' + Resnet50_predict_breed(img_path)\n",
    "    else:\n",
    "        predict_result = 'predict error!!!'\n",
    "    print(predict_result + '\\n')\n",
    "    print('======================')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "## 步骤 7: 测试你的算法\n",
    "\n",
    "在这个部分中，你将尝试一下你的新算法！算法认为__你__看起来像什么类型的狗？如果你有一只狗，它可以准确地预测你的狗的品种吗？如果你有一只猫，它会将你的猫误判为一只狗吗？\n",
    "\n",
    "\n",
    "<a id='question11'></a>  \n",
    "\n",
    "### __问题 11:__\n",
    "\n",
    "在下方编写代码，用至少6张现实中的图片来测试你的算法。你可以使用任意照片，不过请至少使用两张人类图片（要征得当事人同意哦）和两张狗的图片。\n",
    "同时请回答如下问题：\n",
    "\n",
    "1. 输出结果比你预想的要好吗 :) ？或者更糟 :( ？\n",
    "2. 提出至少三点改进你的模型的想法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a dog, it looks like a... \n",
      "Alaskan_malamute\n",
      "\n",
      "======================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a dog, it looks like a... \n",
      "Chow_chow\n",
      "\n",
      "======================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a dog, it looks like a... \n",
      "German_shepherd_dog\n",
      "\n",
      "======================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a dog, it looks like a... \n",
      "American_eskimo_dog\n",
      "\n",
      "======================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict error!!!\n",
      "\n",
      "======================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello human, you look like a ... \n",
      "Akita\n",
      "\n",
      "======================\n"
     ]
    }
   ],
   "source": [
    "## TODO: 在你的电脑上，在步骤6中，至少在6张图片上运行你的算法。\n",
    "## 自由地使用所需的代码单元数吧\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import imread\n",
    "import os \n",
    "\n",
    "pre_img_path = os.path.abspath('./predict_images/')\n",
    "\n",
    "# 六张测试图片，包含4条狗，2个人\n",
    "imgs = ['001_husky.jpg', '002_tibetan_mastiff.jpg', '003_german_shepherd.jpg',\n",
    "       '004_samo_dog.jpg', '005_black_people.jpg', '006_white_people.jpg']\n",
    "\n",
    "def test_model(img_path):\n",
    "    test_img = matplotlib.pyplot.imread(img_path)\n",
    "    plt.imshow(test_img)\n",
    "    plt.show()\n",
    "    cnn_predict_func(img_path)\n",
    "    \n",
    "for x in imgs:\n",
    "    img_path = pre_img_path + '/' + x\n",
    "    test_model(img_path)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、输出结果比你预想的要好吗 :) ？或者更糟 :( ？\n",
    "\n",
    "- 比预期要差些\n",
    "\n",
    "2、提出至少三点改进你的模型的想法。\n",
    "\n",
    "- 对训练集进行数据增强，以优化模型的表现\n",
    "- 对输入图片进行预处理，尽量保持测试数据与训练数据一致或相似\n",
    "- 使用具有更多训练集，准确率更高的CNN迁移学习模型特征库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意: 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出File -> Download as -> HTML (.html)把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。**"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
